WO2017041730A1 - Method and system for navigating mobile robot to bypass obstacle - Google Patents

Method and system for navigating mobile robot to bypass obstacle Download PDF

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Publication number
WO2017041730A1
WO2017041730A1 PCT/CN2016/098460 CN2016098460W WO2017041730A1 WO 2017041730 A1 WO2017041730 A1 WO 2017041730A1 CN 2016098460 W CN2016098460 W CN 2016098460W WO 2017041730 A1 WO2017041730 A1 WO 2017041730A1
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Prior art keywords
robot
node
open
execute
path
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PCT/CN2016/098460
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French (fr)
Chinese (zh)
Inventor
王玉亮
王晓刚
王巍
薛林
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北京进化者机器人科技有限公司
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Priority to CN201510571370.5A priority patent/CN105116902A/en
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Publication of WO2017041730A1 publication Critical patent/WO2017041730A1/en

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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions

Abstract

A method for navigating a mobile robot to bypass an obstacle. The method comprises: establishing a global map (101); setting a starting point and a finishing point (102); planning a path according to an A* algorithm (103); detecting the position of an obstacle (104); replanning the path according to the A* algorithm (105); controlling a robot to move (106); determining whether the robot reaches the finishing point (107); and stopping moving (108).Also provided is a system for navigating a mobile robot to bypass an obstacle.The present invention ensures the accuracy and effectiveness of navigating a robot to bypass an obstacle.

Description

Method and system for obstacle avoidance navigation of mobile robot Technical field

The present invention relates to the field of automation technologies, and in particular, to a method and system for obstacle avoidance navigation of a mobile robot.

Background technique

The development of robotics is the common crystallization of the comprehensive development of science and technology. According to the purpose, the robot can be divided into military robots, industrial robots, service robots, etc. Among them, there are huge demands for mobile robots among these robot types.

The research scope of mobile robots covers architecture, control mechanism, information system, sensing technology, planning strategy, and drive system, including mechanical kinematics, artificial intelligence, intelligent control, pattern recognition, image processing, and visual technology. , a variety of subject areas such as sensor technology, computer networks and communications, and even bioinformatics. Mobile robots are widely used not only in industries such as industry, agriculture, medical care, and services, but also in hazardous and dangerous environments such as urban security, defense, and space exploration. The research level of mobile robots is an important indicator to measure a country's level of scientific and technological development and comprehensive national strength. The "robot revolution" is expected to be an entry point and an important growth point for the "third industrial revolution", which will affect the global manufacturing landscape. The International Federation of Robotics (IFR) predicts that the "robot revolution" will create a trillion-dollar market, leading to the rapid development of key technologies and markets such as new material functional modules related to robots, sensory acquisition and recognition, intelligent control and navigation.

Intelligent robots, such as sweeping robots and home service robots, are increasingly used in industrial production and family life. Robots need to have autonomous navigation capabilities to achieve flexible, efficient, and intelligent movement. Autonomous obstacle avoidance technology is a key indicator for evaluating the intelligence degree of robots, which reflects the ability to deal with unknown obstacles. It is also the key technology for intelligent robots to complete preset tasks in the location environment. One. The mobile robot is in an unknown, complex and dynamic unstructured environment. Without human intervention, it should have the ability to use its own sensors to sense the information about its environment, and model the environment. Open obstacles while minimizing time and energy consumption.

In the field of robotic obstacle avoidance navigation technology, domestic and foreign scholars have proposed effective solutions. Ultrasonic sensors are used to detect obstacles, using compass positioning, and Bayesian probability algorithm to calculate the probability of obstacle occupancy, thus achieving environmental information monitoring and path planning. The optimal control technology uses visual feedback to solve the obstacle avoidance problem of the robot. The robot based on the image target image minimum scheme is used to control the robot. The dynamic quasi-Newton method is used to perform dynamic recursive least square Jacobian estimation to achieve the objective function. Minimize. By using a monocular camera to obtain the approximate three-dimensional information of the obstacle, the ultrasonic sensor is used to acquire accurate information of the obstacle, and the monocular vision and the ultrasonic wave are used together to detect the obstacle information. The intelligent mobile robot platform developed by the Chinese Academy of Sciences - Aim, has comprehensive functions such as visual tracking, voice dialogue, and autonomous obstacle avoidance. It is equipped with 16 ultrasonic sensors and 16 infrared sensors to detect obstacles.

The existing robotic autonomous obstacle avoidance navigation technology has many shortcomings such as complicated structure, high hardware cost and high maintenance cost, and is not suitable for the rapid development of robot development.

Summary of the invention

The invention provides a method and a system for obstacle avoidance navigation of a mobile robot, which can detect an unknown environment, acquire information of unknown obstacles, and introduce an estimation function to plan the shortest and most economical path, thereby saving robot obstacle avoidance navigation. Equipment cost. The program can also monitor the position and moving posture of the robot in real time, and adjust and control the walking state of the robot in real time and dynamically according to the deviation of the robot and the planning path, thus ensuring the accuracy and effectiveness of the obstacle avoidance navigation of the robot.

The technical solution of the present invention provides a method for obstacle avoidance navigation of a mobile robot, comprising the following steps:

Establish a global map of the home environment;

Set the start and end points of the robot movement;

Plan the moving path of the robot according to the A* algorithm;

Marking the location of the obstacle in the global map;

Re-planning the movement path of the robot according to the A* algorithm;

Controlling robot movement according to the planned path;

When the robot reaches the end, it stops moving.

Further, the A* algorithm includes the following steps:

A, put the starting point s into the open table;

B. Traversing the child nodes in eight directions around the s node;

C. Determine whether the eight child nodes are in the open table or the close table;

If the child node is in the open table, execute D;

If the child node is in the close table, execute F;

If the child node is not in the open or close table, execute H;

D. Recalculate the value of the node h(n)+g(n) in the open table, and determine whether to decrease;

If it decreases, execute E;

If no reduction occurs, execute I;

E, update the h(n)+g(n) value of the node in the open table, and turn to I;

F. Recalculate the h(n)+g(n) values of the nodes in the close table, and determine whether to decrease;

If it is decreased, execute G;

If no reduction occurs, execute I;

G, the child node is removed from the close table, placed in the open table, and turned to I;

H. Calculate the value of the child node h(n)+g(n) and add it to the open table;

I, sort according to the value of h (n) + g (n), select the node with the smallest value into the close table;

J. Determine whether h(n) is 0;

If it is 0, execute K;

If not 0, execute B;

K, find the end.

The open table is used to store nodes that have been generated but not examined;

The closed table is used to record nodes that have already been visited.

Further, the calculation method of the f(n) value is:

The adjacent nodes of node n have eight search directions, namely upper, lower, left, right, upper left, lower left, upper right, and lower right;

For each search direction, an estimation function is used to calculate an estimate from the current point to the next point, and the direction in which the estimate is minimized is set to the next direction of motion.

Further, the estimation function is

f(n)=g(n)+h(n)

among them,

f(n) is the estimated value from the current point to the next point.

g(n) is the actual value from the starting point s to the node n, representing the priority trend of the search breadth.

h(n) is an estimate of the best path from node n to target point D, containing heuristic information in the search.

Further, the obstacle is detected using ultrasonic waves, and the position of the obstacle in the robot coordinate system is converted into a position in the global map;

Set the obstacle detection threshold to 1500mm, do not process more than 1500mm, and mark obstacles in the global map if it is less than 1500mm.

Further, the planned path is represented by a two-dimensional array;

The number of rows of the two-dimensional array represents the number of straight segments in the path, and the number of columns represents the position of the grid in each straight segment;

The point in the path defined by the two-dimensional array is a local target point.

Further, the controlling the movement of the robot according to the planned path further includes:

Updating the position and posture of the robot in real time during the movement;

Calculate the deviation between the current position of the robot and the local target point, correct the deviation in real time during the walking process, and realize real-time control of the robot.

The technical solution of the present invention further provides a system for obstacle avoidance navigation of a mobile robot, comprising: a control unit, an odometer, an ultrasonic sensor, and an attitude sensor, wherein

The control unit is used to store and adjust the map, calculate the A* algorithm, control the movement of the robot, and correct the deviation of the robot movement;

The odometer is used to measure the distance the robot walks indoors;

An ultrasonic sensor is used to detect obstacle information around the robot;

The attitude sensor is used to detect the attitude and direction of movement of the robot.

Further, the control unit receives the measurement data from the odometer and calculates the position of the robot;

The control unit calculates a deviation from the local target point in the planned path according to the position of the robot.

Further, the control unit receives the measurement data of the attitude sensor to obtain the posture and the moving direction of the robot;

The control unit controls the motion of the robot in real time based on the calculated deviation and the posture of the robot.

The technical solution of the present invention provides a method and system for obstacle avoidance navigation of a mobile robot, which can detect an unknown environment, acquire information of unknown obstacles, and use an estimation function to plan the shortest and most economical path, thereby saving robot obstacle avoidance. Equipment cost of the navigation system. The program can also monitor the position and moving posture of the robot in real time, and adjust and control the walking state of the robot in real time and dynamically according to the deviation of the robot and the planning path, thus ensuring the accuracy and effectiveness of the obstacle avoidance navigation of the robot.

Other features and advantages of the invention will be set forth in the description which follows, The objectives and other advantages of the invention may be realized and obtained by means of the structure particularly pointed in the appended claims.

The technical solution of the present invention will be further described in detail below through the accompanying drawings and embodiments.

DRAWINGS

The drawings are intended to provide a further understanding of the invention, and are intended to be a In the drawing:

1 is a flowchart of a method for obstacle avoidance navigation of a mobile robot according to Embodiment 1 of the present invention;

2 is a schematic diagram of calculation of an estimation function f(n) according to Embodiment 1 of the present invention;

3 is a schematic diagram of eight search directions adjacent to node a in the first embodiment of the present invention;

4 is a flowchart of a method for planning a moving path of a robot according to an A* algorithm according to Embodiment 2 of the present invention;

FIG. 5 is a structural diagram of a mobile robot obstacle avoidance navigation system according to Embodiments 1 to 2 of the present invention.

detailed description

The preferred embodiments of the present invention are described with reference to the accompanying drawings, which are intended to illustrate and illustrate the invention.

Embodiment 1: A method for mobile robot obstacle avoidance navigation.

FIG. 1 is a flowchart of a method for obstacle avoidance navigation of a mobile robot according to Embodiment 1 of the present invention. As shown in Figure 1, the process includes the following steps:

Step 101: Establish a global map of the home environment.

The global map is the range of activities in which the robot is located, and the information such as the origin of the coordinates, obstacles, and range of activities are identified;

The global map is a grid map, a network of graphs consisting of a series of square grids that mark information about the indoor environment;

The raster map records the position of the grid in abscissa (X coordinates) and ordinate (Y coordinates), and records the probability that each grid is occupied by obstacles by CV value.

Step 102: Set a starting point and an ending point of the robot movement.

Manually enter the start and end points of the robot movement, or set the robot's fixed task Point and end point, or arrange the start and end points for the robot through speech recognition;

The robot initiates the task, moving from the starting point to the end point.

Step 103: Plan a moving path of the robot according to the A* algorithm.

The A* algorithm includes the following steps:

A, put the starting point s into the open table;

B. Traversing the child nodes in eight directions around the s node;

C. Determine whether the eight child nodes are in the open table or the close table;

If the child node is in the open table, execute D;

If the child node is in the close table, execute F;

If the child node is not in the open or close table, execute H;

D. Recalculate h(n)+g(n) and judge whether it is reduced;

If it decreases, execute E;

If no reduction occurs, execute I;

E, update the h(n)+g(n) value of the node in the open table, and turn to I;

F. Recalculate the h(n)+g(n) values of the nodes in the close table, and determine whether to decrease;

If it is decreased, execute G;

If no reduction occurs, execute I;

G, the child node is removed from the close table, placed in the open table, and turned to I;

H. Calculate the value of the child node h(n)+g(n) and add it to the open table;

I, sort according to the value of h (n) + g (n), select the node with the smallest value into the close table;

J. Determine whether h(n) is 0;

If it is 0, execute K;

If not 0, execute B;

K, find the end.

The open table is used to store nodes that have been generated but not examined;

The closed table is used to record nodes that have already been visited.

Wherein, the calculation method of the estimated function f(n) value is:

The adjacent nodes of node n have eight search directions, namely upper, lower, left, right, upper left, lower left, upper right, and lower right;

For each search direction, an estimation function is used to calculate an estimate from the current point to the next point, and the direction in which the estimate is minimized is set to the next direction of motion;

The estimated value of node n is

f(n)=g(n)+h(n)

among them,

f(n) is the estimated value from the current point to the next point.

g(n) is the actual value from the starting point s to the node n, representing the priority trend of the search breadth.

h(n) is an estimate of the best path from node n to target point D, containing heuristic information in the search.

The planned path is represented by a two-dimensional array;

The number of rows of the two-dimensional array represents the number of straight segments in the path, and the number of columns represents the position of the grid in each straight segment;

The point in the path defined by the two-dimensional array is a local target point.

Step 104: Detect and mark the location of the obstacle during the movement.

During the movement of the robot, the ultrasonic is used to detect the obstacle information, and the position of the obstacle in the robot coordinates is converted into a position in the global map;

Set the obstacle detection threshold to 1500mm, do not process more than 1500mm, and mark obstacles in the global map if it is less than 1500mm.

Step 105: Re-plan the moving path of the robot according to the A* algorithm.

According to the global map with the obstacle information added, the moving path of the robot is re-planned according to the A* algorithm;

The robot regains the adjusted two-dimensional array to represent the new path.

Step 106: Control robot movement according to the planned path.

The position and attitude information of the robot is updated in real time during the movement through the travel meter and the attitude sensor;

Calculate the deviation between the current position of the robot and the local target point, correct the deviation in real time during the walking process, and realize real-time control of the robot.

Step 107: Determine whether the robot reaches the end point.

Determine if the position and end point of the robot coincide

In step 108, the robot stops moving.

When the robot reaches the end, it stops moving.

Embodiment 2: A method of planning a moving path of a robot according to an A* algorithm.

FIG. 4 is a flowchart of a method for planning a moving path of a robot according to an A* algorithm according to Embodiment 2 of the present invention. As shown in FIG. 4, the method process includes the following steps:

Step 201: Put the starting point s into the open table;

Step 202: Traversing child nodes in eight directions around the s node;

Step 203: Determine whether the eight child nodes are in an open table or a close table.

If the child node is in the open table, step 204 is performed;

If the child node is in the close table, step 206 is performed;

If the child node is not in the open table or the close table, step 208 is performed;

Step 204: Recalculate the value of the node h(n)+g(n) in the open table, and determine whether to decrease;

If the decrease, step 205 is performed;

If no reduction occurs, step 209 is performed;

Step 205, update the h (n) + g (n) value of the node in the open table, and proceeds to step 209;

Step 206, recalculating the value of h(n)+g(n) of the node in the close table, and determining whether to decrease;

If the decrease, step 207 is performed;

If no reduction occurs, step 209 is performed;

Step 207, the child node is removed from the close table, placed in the open table, and proceeds to step 209;

Step 208: Calculate the value of the child node h(n)+g(n) and add it to the open table.

Step 209: Sort according to the value of h(n)+g(n), and select the node with the smallest value to be placed in the close table;

Step 210: Determine whether h(n) is 0;

If it is 0, then step 211 is performed;

If not 0, step 202 is performed;

Step 211, find the end point.

The open table is used to store nodes that have been generated but not examined;

The closed table is used to record nodes that have already been visited.

FIG. 5 is a structural diagram of a mobile robot obstacle avoidance navigation system according to Embodiments 1 to 2 of the present invention. The system includes: a control unit 301, an odometer 302, an ultrasonic sensor 303, and an attitude sensor 304, wherein

The control unit is used to store and adjust the map, calculate the A* algorithm, control the movement of the robot, and correct the deviation of the robot movement;

The odometer is used to measure the distance the robot walks indoors;

An ultrasonic sensor is used to detect obstacle information around the robot;

The attitude sensor is used to detect the attitude and direction of movement of the robot.

Further, the control unit receives the measurement data from the odometer and calculates the position of the robot;

The control unit calculates a deviation from the local target point in the planned path according to the position of the robot.

Further, the control unit receives the measurement data of the attitude sensor to obtain the posture and the moving direction of the robot;

The control unit controls the motion of the robot in real time based on the calculated deviation and the posture of the robot.

The technical solution of the present invention provides a method and system for obstacle avoidance navigation of a mobile robot, which can The environment is detected, the information of unknown obstacles is obtained, and the estimation function is introduced to plan the shortest and most economical path, which saves the equipment cost of robot obstacle avoidance navigation. The program can also monitor the position and moving posture of the robot in real time, and adjust and control the walking state of the robot in real time and dynamically according to the deviation of the robot and the planning path, thus ensuring the accuracy and effectiveness of the obstacle avoidance navigation of the robot.

Those skilled in the art will appreciate that embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) including computer usable program code.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (system), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine for the execution of instructions for execution by a processor of a computer or other programmable data processing device. Means for implementing the functions specified in one or more of the flow or in a block or blocks of the flow chart.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device. The apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

It is apparent that those skilled in the art can make various modifications and variations to the invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the invention are within the scope of the invention The invention is also intended to cover such modifications and variations within the scope of the equivalents.

Claims (10)

  1. A method for obstacle avoidance navigation of a mobile robot, comprising the steps of:
    Establish a global map of the home environment;
    Set the start and end points of the robot movement;
    Plan the moving path of the robot according to the A* algorithm;
    Marking the location of the obstacle in the global map;
    Re-planning the movement path of the robot according to the A* algorithm;
    Controlling robot movement according to the planned path;
    When the robot reaches the end, it stops moving.
  2. The method of claim 1 wherein the A* algorithm comprises the steps of:
    A, put the starting point s into the open table;
    B. Traversing the child nodes in eight directions around the s node;
    C. Determine whether the eight child nodes are in the open table or the close table;
    If the child node is in the open table, execute D;
    If the child node is in the close table, execute F;
    If the child node is not in the open or close table, execute H;
    D. Recalculate the value of the node h(n)+g(n) in the open table, and determine whether to decrease;
    If it decreases, execute E;
    If no reduction occurs, execute I;
    E, update the h(n)+g(n) value of the node in the open table, and turn to I;
    F. Recalculate the h(n)+g(n) values of the nodes in the close table, and determine whether to decrease;
    If it is decreased, execute G;
    If no reduction occurs, execute I;
    G, the child node is removed from the close table, placed in the open table, and turned to I;
    H. Calculate the value of the child node h(n)+g(n) and add it to the open table;
    I, sort according to the value of h (n) + g (n), select the node with the smallest value into the close table;
    J. Determine whether h(n) is 0;
    If it is 0, execute K;
    If not 0, execute B;
    K, find the end.
    The open table is used to store nodes that have been generated but not examined;
    The closed table is used to record nodes that have already been visited.
  3. The method according to claim 1 or 2, wherein the calculation method of the f(n) value is:
    The adjacent nodes of node n have eight search directions, namely upper, lower, left, right, upper left, lower left, upper right, and lower right;
    For each search direction, an estimation function is used to calculate an estimate from the current point to the next point, and the direction in which the estimate is minimized is set to the next direction of motion.
  4. The method according to claim 1 or 4, wherein said estimation function is
    f(n)=g(n)+h(n)
    among them,
    f(n) is the estimated value from the current point to the next point.
    g(n) is the actual value from the starting point s to the node n, representing the priority trend of the search breadth.
    h(n) is an estimate of the best path from node n to target point D, containing heuristic information in the search.
  5. The method of claim 1 further comprising:
    Ultrasonic detection of obstacles and conversion of the position of the obstacle in the robot coordinate system to a position in the global map;
    Set the obstacle detection threshold to 1500mm, do not process more than 1500mm, and mark obstacles in the global map if it is less than 1500mm.
  6. The method of claim 1 further comprising:
    The planned path is represented by a two-dimensional array;
    The number of rows of the two-dimensional array represents the number of straight segments in the path, and the number of columns represents the position of the grid in each straight segment;
    The last point of each row defined by the two-dimensional array is a local target point in the segment path.
  7. The method according to claim 1, wherein the controlling the movement of the robot according to the planned path further comprises:
    Updating the position and posture of the robot in real time during the movement;
    Calculate the deviation between the current position of the robot and the local target point, correct the deviation in real time during the walking process, and realize real-time control of the robot.
  8. A system for obstacle avoidance navigation of a mobile robot, comprising: a control unit, an odometer, an ultrasonic sensor, and an attitude sensor, wherein
    The control unit is used to store and adjust the map, calculate the A* algorithm, control the movement of the robot, and correct the deviation of the robot movement;
    The odometer is used to measure the distance the robot walks indoors;
    An ultrasonic sensor is used to detect obstacle information around the robot;
    The attitude sensor is used to detect the attitude and direction of movement of the robot.
  9. The method of claim 8 further comprising:
    The control unit receives the measurement data from the odometer and calculates the position of the robot;
    The control unit calculates a deviation from the local target point in the planned path according to the position of the robot.
  10. The method of claim 8 further comprising:
    The control unit receives the measurement data of the attitude sensor to obtain the posture and the moving direction of the robot;
    The control unit controls the motion of the robot in real time based on the calculated deviation and the posture of the robot.
PCT/CN2016/098460 2015-09-09 2016-09-08 Method and system for navigating mobile robot to bypass obstacle WO2017041730A1 (en)

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